Over the years, scientists and engineers have designed and implemented methods for machine vision and subsequent response to objects observed in the world with varying degrees of success. Some of the previous engineering methods were based on natural biological vision systems that required a mix of computer science, engineering, and physiology. Biologically inspired vision engineering is one of the most promising research areas for future successes in machine vision applications.
The performance, from an information processing point of view, of people and animals behaving in the real world greatly surpasses the current abilities of the most sophisticated engineered systems for seemingly simple tasks, including basic image understanding, robotic navigation, and obstacle avoidance. Artificial machines do not have to function like a biological system to be useful, but there is obvious evidence that biologically inspired systems actually work better for many engineering problems.
Although the potential benefits of building information processing machines that work like biological systems are clear, the technical details of how to complete such a task are not clear. This field depends on the convergence of neuroscience and engineering. Neuroscience is the field concerned with the way biological systems process information. Electrical and computer engineering is concerned with the implementation of information processing systems. The present invention demonstrates the achievement of an electronic implementation of selected contemporary neuroscience models associated with the mammalian vision system integrated into an engineered system that performs depth detection and subsequent obstacle avoidance.
A designer of a mobile, autonomous robot has many sensors available to detect obstacles and other environmental features. These detector types include sonar, laser range-finding, bump sensing, radar, infrared, and vision; to date, sonar has met with the most success. Sonar is a mature, accurate technology with moderate data processing requirements. Even some biological systems, such as a bat, use sonar for obstacle and target detection.
Despite the limited success of sonar applied to robotics, visual based processing is potentially much more useful. Vision carries more tactical information than one dimensional technologies like sonar, because vision has higher bandwidth and a two dimensional format. Sonar, radar, and laser range-finding are essentially one dimensional technologies unless one applies additional enhancements such as scanning techniques or detector arrays. Those technologies also radiate power, resulting in fundamental limitations on sensor range and vehicle power requirements. Infrared technology is similar to vision, but it uses a different portion of the electromagnetic spectrum. Unlike vision, it also works in the dark, but can only see obstacles that radiate heat.
Vision is a superior strategy as it does not suffer from any of the above problems. Vision is passive. Sensor range is limited only by the scene and environmental factors, and power requirements are limited only by the information processing technology. Unfortunately, robotic vision requires large amounts of data processing, and engineers have not yet discovered how to extract adequate information for robot navigation, regardless of computational limitations. However, the majority of biological systems have evolved to handle the data processing requirements with tremendous success. Casual observation of humans or almost any animal will show that vision enables sophisticated, fast behavior that engineers have not duplicated in previous machines.
The present invention method involves the electronic processing of algorithms that model the neural processes that allow humans to observe objects and move around them. The emulation of biological systems as carried out by the present invention is implemented by technologies currently available, despite the fact that these technologies support limited interconnection topologies and parallelism as compared to real neural systems.
Various objects, advantages and novel features of the invention will be set forth in part in the description which follows, and in part will become apparent to those skilled in the art upon examination of the following or may be learned by practice of the invention. The objects and advantages of the invention may be realized and attained by means of the instrumentalities and combinations particularly pointed out in the appended claims.